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TinyML System for Touch Modality Classification Based on Multisensory Glove | IEEE Conference Publication | IEEE Xplore

TinyML System for Touch Modality Classification Based on Multisensory Glove


Abstract:

This paper presents a TinyML system based on a multisensory glove for touch modality classification. It employs a glove equipped with five tactile sensors and five IMUs t...Show More

Abstract:

This paper presents a TinyML system based on a multisensory glove for touch modality classification. It employs a glove equipped with five tactile sensors and five IMUs to collect a dataset for five different touch modalities used to train a neural network classifier based on a one-dimensional convolution neural network (1-D CNN). Classification accuracy demonstrated the effectiveness of the proposed model achieving an accuracy of 100% and 98% when float32 and int8 data representation were used respectively. Moreover, the proposed 1-D CNN model is deployed for the low-cost STM32 F401-RE for real-time classification. Hardware evaluation results demonstrate that the system achieves a low energy consumption of 0.636 mJ for int8 with a latency of 1.125 ms. The system paves the way toward intelligent and energy-autonomous wearable systems.
Date of Conference: 28-29 November 2024
Date Added to IEEE Xplore: 20 January 2025
ISBN Information:
Conference Location: Beirut, Lebanon

References

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